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胡凯 等: 基于端到端深度神经网络和图搜索的              OCT  图像视网膜层边界分割方法                           3049


                  [4]  Drexler W, Fujimoto JG. Optical Coherence Tomography: Technology and Applications. Cham: Springer Int’l Publishing, 2015. 3–64.
                     [doi: 10.1007/978-3-319-06419-2]
                  [5]  Fujimoto J, Swanson E. The development, commercialization, and impact of optical coherence tomography. Investigative Ophthalmology
                     & Visual Science, 2016, 57(9): OCT1–OCT13. [doi: 10.1167/iovs.16-19963]
                  [6]  Puliafito CA, Hee MR, Lin CP, Reichel E, Schuman JS, Duker JS, Izatt JA, Swanson EA, Fujimoto JG. Imaging of macular diseases with
                     optical coherence tomography. Ophthalmology, 1995, 102(2): 217–229. [doi: 10.1016/S0161-6420(95)31032-9]
                  [7]  Keane PA, Liakopoulos S, Jivrajka RV, Chang KT, Alasil T, Walsh AC, Sadda SR. Evaluation of optical coherence tomography retinal
                     thickness parameters for use in clinical trials for neovascular age-related macular degeneration. Investigative Ophthalmology & Visual
                     Science, 2009, 50(7): 3378–3385. [doi: 10.1167/iovs.08-2728]
                  [8]  Malamos P, Ahlers C, Mylonas G, Schütze C, Deak G, Ritter M, Sacu S, Schmidt-Erfurth U. Evaluation of segmentation procedures
                     using spectral domain optical coherence tomography in exudative age-related macular degeneration. Retina, 2011, 31(3): 453–463. [doi:
                     10.1097/IAE.0b013e3181eef031]
                  [9]  Bavinger JC, Dunbar GE, Stem MS, Blachley TS, Kwark L, Farsiu S, Jackson GR, Gardner TW. The effects of diabetic retinopathy and
                     pan-retinal  photocoagulation  on  photoreceptor  cell  function  as  assessed  by  dark  adaptometry.  Investigative  Ophthalmology  &  Visual
                     Science, 2016, 57(1): 208–217. [doi: 10.1167/iovs.15-17281]

                 [10]  Sarhan  MH,  Nasseri  MA,  Zapp  D,  Maier  M,  Lohmann  CP,  Navab  N,  Eslami  A.  Machine  learning  techniques  for  ophthalmic  data
                     processing: A review. IEEE Journal of Biomedical and Health Informatics, 2020, 24(12): 3338–3350. [doi: 10.1109/JBHI.2020.3012134]
                 [11]  Ngo L, Yih G, Ji S, Han JH. A study on automated segmentation of retinal layers in optical coherence tomography images. In: Proc. of
                     the 4th Int’l Winter Conf. on Brain-computer Interface (BCI). Gangwon: IEEE, 2016. 1–2. [doi: 10.1109/IWW-BCI.2016.7457465]
                 [12]  Ishikawa H, Stein DM, Wollstein G, Beaton S, Fujimoto JG, Schuman JS. Macular segmentation with optical coherence tomography.
                     Investigative Ophthalmology & Visual Science, 2005, 46(6): 2012–2017. [doi: 10.1167/iovs.04-0335]
                 [13]  He QY, Li ZL, Wang XC, Nan N, Lu Y. Automated retinal layer segmentation based on optical coherence tomographic images. Acta
                     Optica Sinica, 2016, 36(10): 1011003 (in  Chinese  with  English  abstract). [doi: 10.3788/AOS201636.1011003]
                 [14]  Naz  S,  Akram  MU,  Khan  SA.  Automated  segmentation  of  retinal  layers  from  OCT  images  using  structure  tensor  and  kernel
                     regression+GTDP approach. In: Proc. of the 1st Int’l Conf. on Next Generation Computing Applications (NextComp). Mauritius: IEEE,
                     2017. 98–102. [doi: 10.1109/NEXTCOMP.2017.8016182]
                 [15]  Liu YH, Carass A, He YF, Antony BJ, Filippatou A, Saidha S, Solomon SD, Calabresi PA, Prince JL. Layer boundary evolution method
                     for macular OCT layer segmentation. Biomedical Optics Express, 2019, 10(3): 1064–1080. [doi: 10.1364/BOE.10.001064]
                 [16]  El Tanboly A, Ismail M, Switala A, Mahmoud M, Soliman A, Neyer T, Palacio A, Hadayer A, El-Azab M, Schaal S, El-Baz A. A novel
                     automatic  segmentation  of  healthy  and  diseased  retinal  layers  from  OCT  scans.  In:  Proc.  of  the  2016  IEEE  Int ’l  Conf.  on  Image
                     Processing (ICIP). Phoenix: IEEE, 2016. 116–120. [doi: 10.1109/ICIP.2016.7532330]
                 [17]  Tian J, Varga B, Somfai GM, Lee WH, Smiddy WE, DeBuc DC. Real-time automatic segmentation of optical coherence tomography
                     volume data of the macular region. PLoS One, 2015, 10(8): e0133908. [doi: 10.1371/journal.pone.0133908]
                 [18]  Hussain MA, Bhuiyan A, Turpin A, Luu CD, Smith RT, Guymer RH, Kotagiri R. Automatic identification of pathology-distorted retinal
                     layer boundaries using SD-OCT imaging. IEEE Trans. on Biomedical Engineering, 2017, 64(7): 1638–1649. [doi: 10.1109/TBME.2016.
                     2619120]
                 [19]  Niu SJ, Chen Q, Lu ST, Shen HL. SD-OCT image layer segmentation using multi-scale 3-D graph search method. Computer Science,
                     2015, 42(9): 272–277 (in  Chinese  with  English  abstract). [doi: 10.11896/j.issn.1002-137X.2015.9.053]
                 [20]  Stankiewicz  A,  Marciniak  T,  Dabrowski  A,  Stopa  M,  Rakowicz  P,  Marciniak  E.  Novel  full-automatic  approach  for  segmentation  of
                     epiretinal  membrane  from  3D  OCT  images.  In:  Proc.  of  the  2017  Signal  Processing:  Algorithms,  Architectures,  Arrangements,  and
                     Applications (SPA). Poznan: IEEE, 2017. 100–105. [doi: 10.23919/SPA.2017.8166846]
                 [21]  Vermeer KA, Van der Schoot J, Lemij HG, De Boer JF. Automated segmentation by pixel classification of retinal layers in ophthalmic
                     OCT images. Biomedical Optics Express, 2011, 2(6): 1743–1756. [doi: 10.1364/BOE.2.001743]
                 [22]  Lang A, Carass A, Hauser M, Sotirchos ES, Calabresi PA, Ying HS, Prince JL. Retinal layer segmentation of macular OCT images using
                     boundary classification. Biomedical Optics Express, 2013, 4(7): 1133–1152. [doi: 10.1364/BOE.4.001133]
                 [23]  Lang  A,  Carass  A,  Bittner  AK,  Ying  HS,  Prince  JL.  Improving  graph-based  OCT  segmentation  for  severe  pathology  in  Retinitis
                     Pigmentosa  patients.  In:  Proc.  of  the  2017  SPIE  Medical  Imaging  Conf.  on  Biomedical  Applications  in  Molecular,  Structural,  and
                     Functional Imaging. Orlando: SPIE, 2017. 101371M. [doi: 10.1117/12.2254849]
                 [24]  Nath  SS,  Anoop  BK,  Sankar  P.  Classification  of  outer  retinal  layers  based  on  KNN-classifier.  In:  Proc.  of  the  2018  Int ’l  Conf.  on
                     Emerging Trends and Innovations in Engineering and Technological Research (ICETIETR). Ernakulam: IEEE, 2018. 1–4. [doi: 10.1109/
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